Detection and determinants of Escherichia coli O157:H7 in Alberta feedlot pens immediately prior to slaughter
Bibliographic record
Abstract
Food safety risks due to Escherichia coli O157:H7 may be affected by variability in prevalence in or on live cattle at slaughter. Our objectives were to assess the prevalence and risk factors associated with E. coli O157:H7 in feedlot pens immediately prior to slaughter, and assess relationships among methods of monitoring the E. coli O157:H7 status of pre-harvest pens. We studied 84 pens containing a total of nearly 27 000 head of cattle in commercial feedlots in Alberta during 2003 and 2004. Sampling devices (ROPES) prepared from manila ropes were used to detect high prevalence pens. Forty of 84 pens (48%) were classified ROPES-positive. Within pens, fecal prevalence ranged between 0% to 80% (median = 20%) and the hide prevalence ranged between 0% and 30% (median = 0%). Pens that were ROPES-positive had a higher median prevalence for feces (40%) and for hides (3.8%) than those that were ROPES-negative (13.3% and 0%, respectively). The prevalence of E. coli O157:H7 in pens immediately prior to slaughter was found to be quite high or very low even within feedlots and seasons. Factors such as sampling month, temperature, precipitation, pen floor conditions, and water tank cleanliness were associated with E. coli O157:H7 outcome measures, although associated factors were not completely consistent among years and outcome measures. Fecal and hide prevalence are considered primary pre-harvest indicators of potential carcass contamination, but other methods such as ROPES that are associated with these outcomes may provide logistic advantages to efficiently classify pens of cattle as high or low risk to food safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".